Download Molecular Evolution in Nonrecombining Regions of the Drosophila

GBE
Molecular Evolution in Nonrecombining Regions of the
Drosophila melanogaster Genome
José L. Campos*, Brian Charlesworth, and Penelope R. Haddrill
Institute of Evolutionary Biology, School of Biological Sciences, University of Edinburgh, United Kingdom
*Corresponding author: E-mail: [email protected].
Accepted: 17 January 2012
Abstract
Key words: recombination, crossing-over, background selection, Hill–Robertson interference, codon usage bias,
heterochromatin.
Introduction
Levels of variation and rates of evolution in different regions
of the genome may be greatly affected by differences in the
frequency of recombination, as a result of the process of
Hill–Robertson interference (HRI) (Hill and Robertson
1966; Felsenstein 1974; Gordo and Charlesworth 2001;
Comeron et al. 2008; Charlesworth et al. 2010), whereby
evolutionary processes at a given site in the genome are
influenced by selection acting on closely linked sites. To a
good approximation, this can be viewed as a reduction in
effective population size (Ne) at the site in question, caused
by the variance in fitness at linked sites subject to selection
(Charlesworth et al. 2010). This effect is likely to be maximal
in regions with little or no genetic recombination because
recombination reduces the intensity of HRI, increasing the
Ne of a region, and hence the efficacy of selection.
These theoretical predictions are consistent with the
observed lower rates of adaptive evolution, higher levels
of fixation of deleterious mutations, and reduced levels of
neutral or nearly neutral variability in genomic regions or
populations with little or no genetic recombination (Comeron
et al. 1999; Charlesworth 2003; Bachtrog 2005; Presgraves
2005; Bartolomé and Charlesworth 2006; Haddrill et al.
2007; Bachtrog et al. 2008; Arguello et al. 2010; Qiu
et al. 2011).
In Drosophila, studies have focused on comparisons of
regions of the genome that apparently lack crossing-over
with regions where crossovers are known to occur (Comeron
et al. 1999; Bachtrog 2005; Presgraves 2005; Bartolomé and
Charlesworth 2006; Haddrill et al. 2007; Bachtrog et al.
2008; Arguello et al. 2010). It is unclear at present whether
gene conversion is also lacking in these regions (Betancourt
et al. 2009; Arguello et al. 2010); for simplicity, we refer
to them here as ‘‘nonrecombining regions.’’ The following
features of such nonrecombining regions in Drosophila have
been found: elevated between-species sequence divergence at nonsynonymous sites and in long introns, reduced
ª The Author(s) 2012. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/
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278
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We study the evolutionary effects of reduced recombination on the Drosophila melanogaster genome, analyzing more than
200 new genes that lack crossing-over and employing a novel orthology search among species of the melanogaster
subgroup. These genes are located in the heterochromatin of chromosomes other than the dot (fourth) chromosome.
Noncrossover regions of the genome all exhibited an elevated level of evolutionary divergence from D. yakuba at
nonsynonymous sites, lower codon usage bias, lower GC content in coding and noncoding regions, and longer introns.
Levels of gene expression are similar for genes in regions with and without crossing-over, which rules out the possibility that
the reduced level of adaptation that we detect is caused by relaxed selection due to lower levels of gene expression in the
heterochromatin. The patterns observed are consistent with a reduction in the efficacy of selection in all regions of the
genome of D. melanogaster that lack crossing-over, as a result of the effects of enhanced Hill–Robertson interference.
However, we also detected differences among nonrecombining locations: The X chromosome seems to exhibit the weakest
effects, whereas the fourth chromosome and the heterochromatic genes on the autosomes located most proximal to the
centromere showed the largest effects. However, signatures of selection on both nonsynonymous mutations and on codon
usage persist in all heterochromatic regions.
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Molecular Evolution in Nonrecombining Regions
Materials and Methods
Gene Partitioning
We divided the D. melanogaster genome into four recombination categories: high (H), intermediate (I), low (L), and
no recombination (N). These divisions are based on their
cytological location (Charlesworth 1996) and the recently
annotated heterochromatic regions (Smith et al. 2007) (supplementary table 1, Supplementary Material online), which
include a much larger set of nonrecombining genes than
those in Haddrill et al. (2007). We used release 5.34 of
the D. melanogaster genome, available in FlyBase (Tweedie
et al. 2009), to download all the currently annotated genes.
We also partitioned the whole data set by chromosome
type: Autosomal (A) and X chromosome (X). Within the nonrecombining genes, we subdivided genes into three categories: nonfourth autosomal (No), fourth chromosome (N4),
and the X chromosome genes (NX). The latter was subdivided into nonrecombining genes near the centromere
and nonrecombining genes near the telomere. We also
contrasted the patterns observed in genes located in the
beta-heterochromatin (contiguous to the euchromatin,
see Miklos and Cotsell 1990), to those located in the
alpha-heterochromatin, which constitutes the majority of
the centromeric heterochromatin (Miklos and Cotsell
1990). The latter was termed ‘‘scaffold heterochromatin’’
in the DHGP and is comprised of internal scaffolds that have
been cytologically localized to an arm and are located proximal to the centromere relative to the beta-heterochromatin.
Finally, we also compared distal and proximal autosomal
beta-heterochromatin genes using the mid-coordinate within
each region as a boundary (see supplementary table 1,
Supplementary Material online).
For each gene with multiple transcripts, we chose the one
that showed the highest transcript score (available for each
transcript reported in FlyBase). In the case of equal scores,
we randomly selected an isoform.
Search for Homologous Heterochromatic Sequences
We found 401 coding genes in the heterochromatic/nonrecombining regions in release 5.34 of the D. melanogaster
genome (regions shown in supplementary table 1, Supplementary Material online). To detect orthologs of these
genes, we first carried out gene annotations of sequences
homologous to these genes in another five species of the
melanogaster group (D. ananassae, D. erecta, D. sechelia,
D. simulans, and D. yakuba). We used genBlastG to perform
the search and gene annotation, which uses a homologybased gene predictor approach (She et al. 2011). We used
the protein sequence of the 401 protein-coding genes
located in the heterochromatin as the input query; as the
target, we used each of the ‘‘chromosome’’ DNA Fasta files
available in FlyBase for each melanogaster group species.
We used an e value cutoff of 10!20, the filtering option
(-f T), coverage of 20% (-c 0.2), and the default setting
for the remaining options. Any newly annotated gene
obtained with genBlastG that overlapped with a coding
gene present in FlyBase was excluded.
Ortholog Assignments
We used orthomcl (Li et al. 2003) to cluster the proteomes
of the six Drosophila species, in order to assemble groups
of orthologous and paralogous sequences. We used an
e value cutoff of 10!20 and an inflation value of 1.5. The
proteomes were obtained from FlyBase, and we added
the newly annotated genes obtained with genblastG
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codon usage bias, increased gene length, an increased
level of nonsynonymous polymorphism relative to synonymous polymorphism, and a reduced incidence of positive
selection.
However, in Drosophila melanogaster and its relatives,
these studies have mostly focused on the small dot (fourth)
chromosome, where recombination is minimal or completely absent (Haddrill et al. 2007; Arguello et al. 2010).
This is because sequence data for most of the other nonrecombining genes were not available because they are in
heterochromatic regions that pose problems for sequencing
and assembly. The discovery of at least 230 protein-coding
genes in the centromeric heterochromatin as a result of the
Drosophila Heterochromatin Genome Project (DHGP, http://
www.dhgp.org/) provides new nonrecombining regions
with which to test the predictions of HRI in genomic regions
outside the fourth chromosome, because many of these
heterochromatic genes are known to have orthologs in
other Drosophila and Dipteran species (Smith et al.
2007). This provides unique and abundant material for an
in-depth analysis of the effects of the nonrecombining environment on patterns of molecular evolution. This should
enable us to exclude the possibility that the features of nonrecombining genes described in Haddrill et al. (2007) and
Larracuente et al. (2008) reflect peculiarities of the set of
genes residing on the fourth chromosome rather than the
effect of reduced recombination. In addition, recent RNAseq
data on gene expression in D. melanogaster provide new
information with which to assess the effects of gene expression on the patterns of molecular evolution in nonrecombining regions (Haddrill et al. 2008; Larracuente et al. 2008).
We have used a data set of more than 10,000 genes from
D. melanogaster and a comparison with the related species
D. yakuba, to examine the effects of recombinational environment on rates and patterns of evolution in coding
genes and on measures of adaptation at the molecular level.
We have thereby extended previous analyses to include
genes in nonrecombining regions on all the autosomal arms
and on the X chromosome.
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Campos et al.
that showed a high homology (e value of 10!20) to any
heterochromatic gene.
Expression Data
Parameters Estimated and Final Data Set
We selected D. yakuba as an outgroup to estimate KA/KS
because its divergence from D. melanogaster is sufficiently
large enough that we avoid any major influence of ancestral
polymorphisms, and its genome is well annotated with
a high coverage (9.1#) (Clark et al. 2007). We chose only
1:1 orthologous genes and performed amino acid sequence
alignments using MAFFT (Katoh et al. 2002). Using the protein alignment and the coding sequence (CDS) obtained
from FlyBase, we made an in-frame CDS alignment using
custom scripts in PERL. All sequence alignments used in this
study are available from the corresponding author upon
request.
We calculated KA/KS using the method of Comeron
(1995) implemented in Gestimator (Thornton 2003). Estimates of the level of codon usage bias from the frequency
of optimal codons, Fop, were calculated using CodonW
(Peden 1999). GC content was estimated for the third positions of codons (GC3) and for the introns of the selected isoform, following removal of 8/30 bp at the beginning/end of
the introns and masking of possible exonic sequences to
exclude any sites that may be subject to selective constraints
within the selected introns. We divided introns into short
($80 bp) and long (.80 bp) following Halligan and Keightley
(2006). As measures of gene length, we used the lengths of
the CDS, short introns (in bp) and long introns (in Kb) for the
selected transcript.
The final data set included only genes with a KS above
0 and below 0.5, amino acid length above 29, percentage
280
Statistical Analyses
We used nonparametric Mann–Whitney U (two-tailed) and
Kruskal–Wallis tests to compare data sets. We controlled
for the false discovery rate (FDR) by using the method of
Benjamini and Hochberg (1995), implemented in the package multtest (Pollard et al. 2005), using a FDR threshold of
0.05, and report only the adjusted P values. For each data set
and parameter, we report the mean and confidence interval
(CI; calculated by bootstrapping across genes), except for
the length variables (CDS length and intron length) where
we report medians, because they are less sensitive to outliers. Patterns of correlations among divergence measures,
codon usage, expression levels, and gene length were analyzed using partial correlations. We calculated partial correlations between two given variables (Fop and KS, expression
and Fop, Fop and CDS length, expression and KA and Fop
and KA) while controlling for their covariates (the variables
other than the pair involved in the correlation), using R function pcor.test (variance–covariance matrix method) available
at http://www.yilab.gatech.edu/pcor (Kim and Yi 2006); we
report Spearman’s nonparametric correlation coefficients
and their 95% CIs obtained from bootstrapping across
genes.
In order to compare pairs of partial correlations between
two data sets of interest, we calculated the CIs of the absolute difference between partial correlations for the nonrecombining and recombining data sets by resampling
the two sets 1,000 times without replacement from the
pooled data set and considered an observed difference between partial correlations to be significant if it fell outside its
95% CI. For partial correlations that showed the same trend
among the four independent nonrecombining regions analyzed (second, third, fourth, and X chromosome), we tested
if such a common trend was significant by combining their
probabilities using Fisher’s combined probability test. P values were combined by adding !2ln(P) for the four nonrecombining data sets. This follows a chi-squared distribution
with eight degrees of freedom (df), which was used to determine the combined P value.
Results
The final data set contained 10,642 genes. We divided them
into autosomal (A) genes and X chromosome (X) genes.
Within the nonrecombining genes, after filtering the initial
Genome Biol. Evol. 4(3):278–288. doi:10.1093/gbe/evs010 Advance Access publication January 23, 2012
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We used RNAseq gene expression available for D. melanogaster in FlyBase (Gelbart and Emmert 2010). For each
D. melanogaster gene, we calculated the mean RNAseq
expression value (expressed as RPKM, reads per kilobase
of exon per million mapped reads) across the 27 temporal
stages of the data set. For each of the adult stages, in the
autosomal genes, we calculated the average of the two
sexes, whereas for genes located on the X chromosome,
we used a weighted average of 2/3 for females and 1/3
for males, which reflects the mean time an X chromosome
spends in each sex.
We also used an alternative measure of gene expression
for the same data set: maximum gene expression at any of
the 27 temporal stages and sexes. This is to take into
account the possibility that a stage/sex-specific estimate
of gene expression could have an important effect on gene
evolution that may not have been detected by using an overall measure of gene expression. We report these two measures of gene expression (overall expression and maximum
expression) data for each gene as log2 (RPKM þ 1).
of amino acid sequence identity above 50%, less than 50%
gaps, presence of a single orthologous gene in D. yakuba,
and with gene expression data (RPKM . 0). We excluded
the few genes present on the Y chromosome and the U
genes (unmapped to any chromosome arm), so we report
only the nonrecombining genes present on a known chromosome. We also excluded nine genes from the scaffold
heterochromatin with the gene status of ‘‘incomplete.’’
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Molecular Evolution in Nonrecombining Regions
Table 1
Comparisons of Recombining Genes and Nonrecombining Genes for the Autosomes and X Chromosome
Autosomal Genes
RA
0.039
0.263
0.138
0.517
0.641
0.354
0.373
9.79
11.76
387
61.33
0.49
8,729
(0.038–0.040)
(0.262–0.265)
(0.136–0.141)
(0.515–0.519)
(0.639–0.643)
(0.351–0.356)
(0.372–0.375)
(9.74–9.84)
(11.71–11.80)
(380–393)
(61.16–61.66)
(0.46–0.51)
0.056
0.276
0.198
0.344
0.441
0.290
0.347
10.24
11.82
454.5
59
1.21
226
(0.050–0.061)
(0.268–0.284)
(0.180–0.215)
(0.326–0.360)
(0.420–0.460)
(0.269–0.309)
(0.339–0.354)
(9.97–10.55)
(11.57–12.07)
(360–502)
(58.33–59.71)
(0.80–1.52)
X Chromosome Genes
P
***
**
***
***
***
***
***
**
ns
***
***
***
RX
0.041
0.258
0.145
0.551
0.688
0.394
0.377
9.90
11.83
407
65.00
0.51
1,645
(0.039–0.043)
(0.254–0.262)
(0.139–0.152)
(0.547–0.555)
(0.684–0.692)
(0.388–0.401)
(0.373–0.381)
(9.80–10.00)
(11.73–11.93)
(389–423)
(64.67–65.30)
(0.44–0.56)
NX
0.047
0.259
0.173
0.452
0.572
0.351
0.339
9.22
11.44
424.5
64
1.22
42
(0.035–0.056)
(0.243–0.276)
(0.134–0.211)
(0.428–0.475)
(0.547–0.594)
(0.320–0.382)
(0.322–0.360)
(8.64–9.86)
(10.80–12.05)
(248–517.5)
(62.67–67)
(0.19–2.13)
P
ns
ns
ns
***
***
ns
***
ns
ns
ns
ns
ns
NOTE.—For each variable in a row, we report the mean and the 95% CI in parentheses, except for the length variables where we use medians. The genome is divided into four
categories: RA, recombining genes in the autosomal regions; NA, nonrecombining genes in autosomal regions; RX, recombining genes on the X chromosome; and NX,
nonrecombining genes on the X chromosome. P: adjusted P value of Mann–Whitney U test (***P , 0.001; **P , 0.01; *P , 0.05; and ns, not significant); GC3, GC content of third
codon positions; GCS, GC content in short introns ($80 bp); GCL, GC content in long introns (.80 bp); Overall exp., total RNAseq expression across all tissues as log2 (mean RPKM þ
1); Max. exp., maximum RNAseq level expression level across all tissues; CDS length, CDS length in number of amino acids; Length short (bp), length of short introns in base pairs; and
Length long (Kb), length of long introns in kilobases.
401 gene data set, we had 268 genes for the analysis. We
subdivided these genes into three categories: nonfourth
chromosome autosomal (No, N 5 159), fourth chromosome
(N4, N 5 67), and X chromosome genes (NX, N 5 42). Of the
nonfourth chromosome genes, 131 genes were located in
the beta-heterochromatin (contiguous to the euchromatin)
and 28 genes were in the alpha-heterochromatin (scaffold
heterochromatin). Among the nonrecombining X chromosome genes, 19 were in the centromeric region and 23 were
at the telomere.
As in Haddrill et al. (2007), the most striking differences
were generally seen in the comparison of recombining regions versus nonrecombining regions. We observed small
differences among regions experiencing high, intermediate,
and low rates of recombination (supplementary table 2,
Supplementary Material online). However, given that the
magnitude of these differences was small compared with
those between recombining and nonrecombining regions,
within each of the autosomal and X-linked data sets, these
three recombining categories were combined into a single
group of recombining genes.
Divergence
The autosomal nonrecombining (NA) regions showed much
higher levels of nonsynonymous divergence (KA), slightly
higher synonymous divergence (KS), and higher KA/KS than
the recombining autosomal (RA) regions (table 1 and fig. 1).
In the X chromosome data set, there were no significant differences between recombining (RX) and nonrecombining
(NX) regions for KA, KS, and KA/KS, although KA/KS showed
a similar trend to the autosomal genes, being higher for the
nonrecombining genes (table 1 and fig. 1). When we contrasted the three groups of genes within nonrecombining
regions (No, N4, and NX), we observed higher synonymous
divergence for No than for NX or N4, which had similar KS
values (table 2 and fig. 1). N4 had an apparently higher KA/
KS than No and NX, and this difference was significant
against the KA/KS of NX by virtue of the N4 genes having
a higher KA and slightly lower KS than the NX genes.
Codon Usage Bias and GC Content
Codon usage bias, as measured by Fop, was significantly
lower for the nonrecombining genes for both the autosomal
and X chromosome data sets (table 1). We also found significant differences among the three nonrecombining categories (table 2); N4 showed the lowest mean Fop and NX the
highest, whereas No was intermediate between N4 and NX.
The GC content at third position coding sites (GC3) showed
the same patterns as for Fop (tables 1 and 2).
We also examined levels of GC content in introns, separating them into short (GCS; $80 bp) and long introns (GCL;
.80 bp). In the autosomal but not the X chromosome data
set, GCS was lower in the nonrecombining regions than in
the recombining category (table 1). There were also significant differences among the nonrecombining regions: NX
and No showed similar levels of GCS content, but both were
higher than for N4 (table 2). For GCL, there was a significantly lower GC content in the nonrecombining category
in both the autosomal and the X-linked data sets (table
1). Among the three nonrecombining categories, there
were highly significant differences between N4 and both
of the other two nonrecombining groups (table 2).
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Number of genes
KA
KS
KA/KS
Fop
GC3
GCS
GCL
Overall exp.
Max. exp.
CDS length (aa)
Length short (bp)
Length long (Kb)
NA
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Campos et al.
not observe gene expression differences between recombining and nonrecombining X-linked genes (table 1). Within
the three different nonrecombining regions, we observed
marginally significantly lower overall levels of expression
for NX than N4 or No (table 2). Similarly, for the autosomes,
but not the X chromosome, there were highly significant differences with respect to the gene length variables. The CDS
length and long introns were longer in the nonrecombining
genes, whereas short introns were slightly shorter on average (table 1). Within the nonrecombining regions, N4 genes
had the longest CDSs, NX the longest short introns, and No
the longest long introns (table 2).
FIG. 1.—Notched box-plots of KA, KS, and KA/KS for: RA,
recombining autosomal genes; RX, recombining X chromosome genes;
No, nonfourth nonrecombining genes; N4, fourth chromosome genes;
and NX, nonrecombining X chromosome genes. The box extends from
the lower to the upper quartile, with a line in the middle at the median.
The dotted bars represent the 5th and 95th percentiles. The notches
represent 95% CIs for the medians.
When comparing all nonrecombining regions separately,
most parameters (except KA, KA/KS, and expression) showed
significant differences among the six nonrecombining regions (chromosomal arms 2L, 2R, 3L, 3R, 4, and X; supplementary table 3, Supplementary Material online). When we
compared genes located in the alpha-heterochromatin
against genes located in the beta-heterochromatin, for
the autosomes, only Fop, GC3, and GCS showed a significant
difference (table 3 and fig. 2), being lower in the alphaheterochromatin. No such differences were found for
the X chromosome, but we only had seven genes in the
X alpha-heterochromatin (data not shown). We found no
differences between distal and proximal autosomal betaheterochromatin or between telomeric and centromeric
nonrecombining genes on the X chromosome (data not
shown).
Gene Expression and Gene Length
There were marginally significantly higher levels of overall
gene expression for nonrecombining compared with RA
genes, but not for maximum expression (table 1). We did
Partial Correlation Analyses
We contrasted well established genome-wide correlations
among divergence, codon usage bias, and expression
Table 2
Comparisons of the Three Nonrecombining Regions
Region
No
Number of genes
KA
KS
KA/KS
Fop
GC3
GCS
GCL
Overall exp.
Max. exp.
CDS length (aa)
Length short (bp)
Length long (Kb)
159
0.056 (0.049–0.063)
0.287 (0.278–0.297)
0.190 (0.169–0.211)
0.379 (0.358–0.399)
0.480 (0.456–0.505)
0.321(0.294–0.345)
0.364 (0.354–0.375)
10.28 (9.93–10.63)
12.00 (11.63–12.37)
384 (322–420)
59 (58.33–60)
2.03 (0.74–2.88)
MW P
N4
67
0.055
0.249
0.217
0.260
0.348
0.228
0.320
10.15
11.61
729
59.33
0.89
(0.047–0.062)
(0.238–0.259)
(0.190–0.246)
(0.247–0.272)
(0.330–0.363)
(0.206–0.251)
(0.311–0.329)
(9.68–10.66)
(11.18–12.04)
(615–875)
(57.99–60.41)
(0.57–1)
NX
42
0.047
0.259
0.173
0.452
0.572
0.351
0.339
9.224
11.44
424.5
64
1.22
(0.035–0.056)
(0.243–0.276)
(0.134–0.211)
(0.428–0.475)
(0.547–0.594)
(0.320–0.382)
(0.322–0.360)
(8.64–9.86)
(10.81–12.06)
(248–517.5)
(62.67–67)
(0.19–2.13)
KW P
No versus N4
No versus NX
N4 versus NX
ns
***
ns
***
***
***
***
*
ns
***
***
*
ns
***
ns
***
***
***
***
ns
ns
***
ns
*
ns
***
ns
**
**
ns
ns
**
ns
ns
***
ns
ns
ns
*
***
***
***
**
*
ns
**
**
ns
NOTE.—The nonrecombining genes are divided into three regions: No, autosomal genes excluding the fourth chromosome; N4, fourth chromosome; and NX, nonrecombining
genes on the X chromosome. KW P: Kruskal–Wallis adjusted P values for No, N4, and NX comparison. MW P, Mann–Whitney U test adjusted P values (***P , 0.001; **P , 0.01; *P
, 0.05; and ns, not significant).
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Individual Nonrecombining Regions
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Molecular Evolution in Nonrecombining Regions
Table 3
Comparisons of Autosomal Alpha- and Beta-Heterochromatin Genes
Region
Beta-Heterochromatin Alpha-Heterochromatin P
131
28
0.055 (0.047–0.063)
0.285 (0.274–0.295)
0.188 (0.161–0.212)
0.404 (0.379–0.426)
0.509 (0.484–0.536)
0.343 (0.318–0.370)
0.368 (0.357–0.378)
10.29 (9.88–10.67)
12.06 (11.67–12.45)
407 (326–455)
59 (58.17–60)
1.65 (0.32–2.28)
0.061 (0.046–0.076)
0.299 (0.273–0.325)
0.201 (0.155–0.245)
0.264 (0.239–0.285)
0.341 (0.314–0.369)
0.228 (0.189–0.260)
0.352 (0.332–0.381)
10.257 (9.346–11.162)
11.75 (10.89–12.60)
261 (104–324.50)
59 (57–62)
5.73 (0–8.48)
ns
ns
ns
***
***
***
ns
ns
ns
ns
ns
ns
NOTE.—P, Mann–Whitney U test adjusted P value (***P , 0.001; **P , 0.01;
*P , 0.05; and ns, not significant).
(Bierne and Eyre-Walker 2006; Drummond and Wilke 2008;
Marion de Procé et al. 2009; Haddrill et al. 2011) between
recombining and nonrecombining regions because we
might expect an absence of recombination to cause these
relationships to break down (Haddrill et al. 2007, 2008).
In general, all partial correlations among these variables
(holding all variables constant other than the pair under consideration) were significant in the recombining groups,
whereas most of these associations were not significant
in the nonrecombining data sets (table 4; for raw correla-
Discussion
Nonsynonymous Divergence
FIG. 2.—GC content of the third position of codons (GC3), short
introns (GCS), and long introns (GCL) for: RA, recombining autosomal
genes; RX, recombining X chromosome genes; NoB, nonfourth betaheterochromatin genes; NoA, nonfourth alpha-heterochromatin genes;
N4, fourth chromosome genes; and NX, nonrecombining X chromosome genes. Values reported are means; error bars indicate 95% CIs
obtained by bootstrapping.
As expected from previous analyses of Drosophila (Bachtrog
2005; Bartolomé and Charlesworth 2006; Haddrill et al.
2007; Bachtrog et al. 2008; Larracuente et al. 2008;
Arguello et al. 2010), KA and KA/KS are significantly higher
in the autosomal nonrecombining regions than in the
recombining regions (table 1). These results are consistent
with less effective selection against weakly deleterious mutations in nonrecombining regions due to increased HRI
when crossing-over is absent (Charlesworth et al. 2010).
Our results largely confirm and extend the conclusions of
Haddrill et al. (2007), although they only found significant
effects of a lack of recombination for the fourth chromosome. In contrast, Arguello et al. (2010) found that autosomal heterochromatic genes behaved similarly to fourth
chromosome genes, in agreement with our results (it is,
however, unclear how many heterochromatic genes were
included in that study). Interestingly, there is no significant
difference in KA or KA/KS between recombining and nonrecombining regions of the X chromosome (table 1), which
may suggest smaller effects of HR interference on this chromosome. Nonetheless, KA and KA/KS are slightly higher for
the NX genes compared with recombining X-linked genes,
Genome Biol. Evol. 4(3):278–288. doi:10.1093/gbe/evs010 Advance Access publication January 23, 2012
283
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Number of
Genes
KA
KS
KA/KS
Fop
GC3
GCS
GCL
Overall exp.
Max. exp.
CDS length (aa)
Length short (bp)
Length long (Kb)
tions, see supplementary fig. 1, Supplementary Material
online). However, we still found a strong association between gene expression and codon usage bias (table 4)
for the nonrecombining autosomal data set (NA) and for
the fourth chromosome (N4). In addition, for the NA, No,
and N3 (nonrecombining third chromosome) genes, there
was a strong negative correlation between gene expression
and KA and between Fop and KA (table 4), whereas the NX
genes only showed a significantly strong negative correlation between Fop and KA. All the partial correlations between expression and KA and between Fop and KA for
the four independent nonrecombining data sets (second,
third, fourth, and X chromosome) showed a negative trend
(table 4). These common negative trends were significant
when using Fisher’s method for combining probabilities
from independent tests (Exp ; KA, v2 5 23.64, df 5 8,
P 5 0.0026; Fop ; KA, v2 5 48.16, df 5 8, P 5 0).
Using the alternative measure maximum gene expression, instead of the overall level of gene expression, we
found the same trends and significant results as above (supplementary table 4, Supplementary Material online), with
the exception of the partial correlation between expression
and KA, which showed no significant results, although the
trend was in the same direction. In addition, Fisher’s method
showed the same results as above in the nonrecombining
regions for the association between expression and KA
(Exp ; KA, v2 5 19, df 5 8, P 5 0.0149) and between codon
usage bias and KA (Fop ; KA, v2 5 49.62, P 5 0).
GBE
Campos et al.
Table 4
Partial Correlations among Divergence Levels, Codon Usage Bias, and Expression
Variables
Region
RA
RX
Exp ; Fop
Fop ; CDS length
Exp ; KA
Fop ; KA
!0.103***
(!0.130/!0.077)
!0.085*
(!0.150/!0.015)
0.139 (0.242)
0.010 (0.113)
0.050 (0.153)
0.051 (0.154)
!0.008 (0.095)
!0.087 (0.002)
Exp, KA, GCS, CDS length
0.298***
(0.272/0.323)
0.240***
(0.180/0.303)
0.213** (0.085)
0.117 (0.181)
0.257 (0.041)
!0.118 (0.416)
0.375** (0.077)
!0.242 (0.482)
KA, KS, GCS, CDS length
!0.188***
(!0.214/!0.162)
!0.276***
(!0.330/!0.216)
!0.046 (0.142)
0.058 (0.246)
0.034 (0.222)
!0.042 (0.146)
0.204 (0.392)
0.019 (0.295)
KA, KS, GCS, Exp
!0.116***
(!0.140/!0.090)
!0.116***
(!0.181/!0.052)
!0.232** (0.116)
!0.238* (0.122)
!0.114 (0.002)
!0.428*** (0.312)
!0.223 (0.107)
!0.226 (0.110)
KS, Fop, GCS, CDS length
!0.307***
(!0.333/!0.285)
!0.246***
(!0.309/!0.188)
!0.205** (0.102)
!0.335** (0.028)
!0.139 (0.168)
!0.539*** (0.232)
!0.201 (0.106)
!0.708*** (0.462)
Exp, KS, GCS, CDS length
NOTE.—We examined eight regions: RA, recombining autosomal genes; RX, recombining X chromosomal genes; NA, nonrecombining autosomal genes; No, nonrecombining
autosomal genes excluding the fourth chromosome; N2, nonrecombining genes on the second chromosome; N3, nonrecombining genes on the third chromosome; N4, fourth
chromosome genes; and NX, nonrecombining X chromosome genes. We show Spearman’s rank partial correlation coefficient and its significance level (***P , 0.001; **P , 0.01;
and *P , 0.05). For the recombining genes, we show the 95% CIs of the Spearman partial correlations in parentheses. For the nonrecombining genes, we show the absolute
difference between partial correlations for the recombining versus nonrecombining categories in parentheses; the differences that are outside the 95% CI limit are shown as
underlined values.
especially in centromeric regions, so that the lack of significance may simply reflect the small number of genes involved.
The similarities in KA and KA/KS among the high, intermediate, and low recombination bins, also found by Haddrill
et al. (2007) and Larracuente et al. (2008), suggest that
relatively low levels of recombination are enough to counteract any HRI effects. This result suggests that rates of
adaptive protein sequence evolution are not higher in regions of high recombination, as has been proposed (Betancourt and Presgraves 2002; Presgraves 2005). Overall, there
seems to be a similar pattern of faster protein sequence evolution in every nonrecombining region of the genome, with
the exception of the X chromosome, which also shows different patterns from the autosomes with respect to codon
usage bias and KS in recombining regions (Singh et al.
2005b, 2008; Vicoso et al. 2008).
Synonymous Divergence and Codon Usage Bias
Synonymous divergence, as measured by KS is also slightly
but significantly higher for nonrecombining than RA genes,
whereas codon usage bias is significantly lower in the nonrecombining regions of both X and autosomes (table 1).
Given the lower levels of codon usage bias and the lack
of a negative correlation between KS and Fop in nonrecombining regions, in contrast to the negative partial correlation
between them in recombining regions (table 4), this suggests that selection on codon usage bias is reduced when
crossing-over is absent, resulting in a higher level of synonymous divergence.
Weakly selected sites are especially susceptible to HRI effects (Charlesworth et al. 2010); as HRI increases in intensity,
they should therefore approach neutrality. The lowest Fop
values are on the fourth chromosome, suggesting that
selection is least efficient on this chromosome, possibly
284
due to a longer history of no crossing-over in this region than
other regions that lack crossing-over (Haddrill et al. 2007), or
a larger concentration of genes in a single genomic region
that lacks crossing-over (KA/KS is also highest for the fourth
chromosome). Surprisingly, despite its lower Fop, KS is lower
for the fourth chromosome than for the other nonrecombining autosomal genes. This may be due to higher synonymous substitution rates in regions that have slightly larger
Ne values than on the fourth chromosome. An increase in
the rate of sequence evolution as Nes increases away from
zero is expected when there is a strong mutational bias toward slightly deleterious variants (Eyre-Walker 1992;
McVean and Charlesworth 1999; Lawrie et al. 2011). This
means that it is possible that slightly higher values of Ne
for the other nonrecombining autosomal genes could result
in a higher rate of synonymous evolution compared with the
fourth chromosome.
The magnitude of the expected reduction in Fop on the
fourth chromosome can be roughly estimated as follows.
Using polymorphism data for an autosomal set of normally
recombining genes, Zeng and Charlesworth (2010) estimated that the mean scaled intensity of selection on preferred codons (c 5 4Nes, where s is the selection
coefficient for heterozygotes for a preferred variant) was
1.29 and the mutational bias (j) toward nonpreferred codons was 2.84 (table 2 in Zeng and Charlesworth
[2010]). Using the Li–Bulmer formula for equilibrium codon
usage (Bulmer 1991), the expected value of Fop is 1/(1 þ j
exp [–c]). The predicted Fop for recombining genes is then
0.56, slightly higher than the value in table 1. Studies of
silent diversity on the fourth chromosome suggest that its
Ne is approximately 10% of that for normally recombining
regions (Arguello et al. 2010; Charlesworth et al. 2010); this
reduces c to 0.13, so that the predicted value of Fop for the
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NA
No
N2
N3
N4
NX
Covariates
Fop ; KS
GBE
Molecular Evolution in Nonrecombining Regions
GC Content
GC content at third coding positions in nonrecombining regions is higher than for introns (fig. 2), which suggests that
some selection is still acting in favor of preferred codons,
which mostly end in GC in Drosophila (Akashi 1994). Zeng
and Charlesworth (2010) also estimated selection and mutational parameters for GC versus AT in intronic sites (mostly
from short introns), obtaining c 5 0.60 and j 5 3.42, giving
a predicted equilibrium value of 0.35, fairly close to the
observed short intronic GC content for RA genes in table
1. The corresponding predicted value for the fourth chromosome is 0.24, similar to the observed value of 0.23 (table 2).
However, the GC content of most other nonrecombining
regions is higher, suggesting that for much of their evolutionary history, their effective population sizes have been
higher than for the fourth chromosome.
It is possible that there is a gradient in the efficiency of
selection on synonymous sites within the nonrecombining
regions. The genes present in the alpha-heterochromatin,
which are located closer to the centromere, have a significantly lower GC3 content than in the beta-heterochromatin
(adjacent to the euchromatin). It therefore seems likely that
the base composition at third position coding sites in the
alpha-heterochromatin is closer to neutral equilibrium than
in other nonrecombining regions. However, there is no
significant difference among the heterochromatic genes adjacent to the euchromatin (beta-heterochromatin) when we
divided these into two groups according to their proximity
to the centromere. The larger effect observed in alphacompared with beta-heterochromatin genes might therefore be due to the location of the former in regions where
recombination is totally absent. It is possible that some recombination due to gene conversion or low residual levels
of crossing-over may occur in the beta-heterochromatin adjacent to the euchromatin, as has been found for the dot
chromosomes (Betancourt et al. 2009; Arguello et al. 2010).
Furthermore, the difference in GC3 between recombining
and nonrecombining regions cannot be caused solely by
lower GC-biased gene conversion in these regions or a higher AT mutational bias because the drop in GC content in
nonrecombining genes is much higher for GC3 (30%) than
for short introns (18% for GCS). Interestingly, the significantly lower short intron GC content in nonrecombining
compared with RA genes suggests that short introns in recombining regions are under some selective constraints,
contrary to what is often assumed (Halligan and Keightley
2006; Parsch et al. 2010).
Gene Expression
There is no evidence for lower expression of genes in any of
the nonrecombining regions in this analysis (except the U
genes, which were excluded from our analyses). Indeed, if
anything, there are slightly higher levels of gene expression
in the nonrecombining compared with the recombining
genes. These results are in the same direction as those observed by Haddrill et al. (2008) who reported significantly
higher gene expression in the genes that lack crossing-over,
using ESTs. However, in contrast to their results, gene expression on the fourth chromosome is similar to that on the
other autosomes, as was also found for the dot chromosome of D. virilis (Betancourt et al. 2009). It follows, therefore, that the higher nonsynonymous divergence and
lower Fop of nonrecombining genes cannot be explained
by the well-documented negative correlation between expression level and nonsynonymous divergence (Marais
et al. 2004; Drummond and Wilke 2008; Larracuente
et al. 2008).
Gene Length
The greater length of long introns in the nonrecombining
versus the recombining genes is consistent with the findings
of Smith et al. (2007), who observed that heterochromatic
introns are enriched in fragmented transposable element
(TE) sequences and show less length conservation in interspecies comparisons. Nonrecombining regions tend to
have longer introns, which are probably due to an
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285
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fourth chromosome is 0.29, again slightly higher than observed. With no selection, the predicted Fop is 0.26, very
close to the fourth chromosome value (table 2).
Fop is also significantly lower for nonrecombining
X-linked genes than in recombining X chromosome regions,
but the reduction in Fop for NX genes is half of that observed
for the autosomal counterparts. Fop is elevated on the
X chromosome relative to that on the autosomes (Singh
et al. 2005b), and this effect is even observed in nonrecombining X-linked versus autosomal regions (table 2). This result differs from that of Singh et al. (2005a), who report
a negative effect of recombination on X chromosome codon
usage, and probably reflects the fact that our X-linked data
set covers a broader range of recombination rates, including
genes in nonrecombining regions, whereas the lowest recombination rate category in their study was 0.27 cM/
Mb. There is evidence from population genetic analyses that
the scaled intensity of selection on preferred versus unpreferred codons, c, is somewhat higher for the freely recombining part of the X chromosome than that for the
autosomes in D. melanogaster (Zeng and Charlesworth
2010). However, if c is reduced in the nonrecombining regions of the X chromosome by the same factor as for the
fourth chromosome, the predicted equilibrium value of
Fop from the estimates of j and c in table 2 of Zeng and
Charlesworth (2010) is around 30%, far lower than the
observed value. This strongly suggests a smaller reduction
in Ne for the nonrecombining part of the X than for the
autosomes.
GBE
Campos et al.
The Effect of a Lack of Recombination on GenomeWide Relationships among Variables
In a completely nonrecombining block of genes, all genes
must be subject to the same intensity of HRI effects. Correlations induced by HRI effects that act specifically within the
gene, such as the postulated effect on codon usage of
amino acid fixations driven by positive selection (Betancourt
and Presgraves 2002; Presgraves 2005), should therefore be
largely absent, especially if there is little or no fixation of positively selected variants in these regions, as suggested by recent results for the dot chromosome (Betancourt et al. 2009;
Arguello et al. 2010). Similarly, if selection on synonymous
sites is greatly reduced by HRI effects throughout a nonrecombining region, relationships between genomic features
that reflect such weak selection (e.g., the negative correlation between Fop and KS) should be greatly reduced in
strength (Haddrill et al. 2007, 2008).
Some of these genome-wide relationships are nonsignificant in nonrecombining regions and significantly smaller
286
than in the corresponding recombining genes (table 4); for
example, the negative partial correlations between Fop
and KS and Fop and CDS length. Nevertheless, within the
nonrecombining regions, some footprints of selection are
observed because there are significant partial correlations
between expression and Fop, expression and KA, and Fop
and KA. There is also a higher GC content in third position
coding sites than introns in nonrecombining regions.
Paradoxically, the negative partial correlation between
expression level and KA is larger in magnitude for nonrecombining than recombining genes, for both the X chromosome
and most of the autosomes. This may reflect selection on
translational accuracy, provided that highly expressed genes
are more selectively constrained than lowly expressed genes
(Akashi 1994; Bierne and Eyre-Walker 2006; Drummond
and Wilke 2008), because the accelerated fixation of slightly
deleterious nonsynonymous mutations is probably most
marked for genes under weak selective constraints, corresponding to those with lower expression levels. This is because of the strongly nonlinear dependence of the
fixation probability of a deleterious mutation on the product
of Ne and the selection coefficient s against a deleterious
mutation (Kimura 1983, p. 44). When Nes .. 1, an increase
in Nes has little effect, but with Nes around 1, there can be
a substantial decrease in the fixation probability of a deleterious mutation as Nes increases.
There has been some controversy regarding the causes of
the negative correlation between KA and codon usage bias,
and whether it is caused by more intense HRI resulting from
more frequent selective sweeps of favorable amino acid
mutations in genes with higher KA values (Betancourt
and Presgraves 2002; Andolfatto 2007) or by stronger selection on translational accuracy in genes with more highly
constrained protein sequences (Akashi 1994; Bierne and
Eyre-Walker 2006; Drummond and Wilke 2008).
The fact that Fop and KA remain negatively associated in
the absence of crossing-over suggests that this relationship
is at least partly caused by correlated differences in levels of
selective constraint on protein sequence and codon usage
across genes and that selection is still partially effective in
the absence of crossing-over. Since we have used partial correlations, these constraints must be independent of gene
expression levels, at least as measured by the methods used
to generate the data we have employed, in contradiction to
the predictions of the model of Drummond and Wilke
(2008). This may simply reflect differences in the overall importance of proteins for cellular functions, with more important proteins showing both higher levels of constraint on
their amino acid sequence and stronger selection for codon
usage.
A significant genome-wide negative partial correlation
between Fop and KA was previously found by Marion de
Procé et al. (2009) and Haddrill et al. (2011), who
interpreted it as potentially indicating an effect of the
Genome Biol. Evol. 4(3):278–288. doi:10.1093/gbe/evs010 Advance Access publication January 23, 2012
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accumulation of TE-derived sequences as a result of weakened counterselection, either due to HRI or a lack of ectopic
exchange for TEs (Bartolomé et al. 2002); overall, TE density
is very high in the heterochromatin (Smith et al. 2007).
Long introns are especially long in the nonfourth chromosome autosomal nonrecombining genes (No), particularly in
the alpha-heterochromatin. The fact that the long introns
on the fourth chromosome are shorter than in other nonrecombining regions is consistent with the lower fraction
of TE-derived DNA, especially retroviral-like elements, on
the fourth chromosome compared with the nonrecombining regions of chromosome 2R and the X chromosome
(Bartolomé et al. 2002; Kaminker et al. 2002), the reasons
for which remain obscure. In contrast, short introns in nonrecombining autosomal regions are slightly smaller than in
autosomal recombining regions, which could be due to relaxed selection on minimal intron length for correct splicing
(Comeron and Kreitman 2000).
Interestingly, only the fourth chromosome shows evidence for longer CDS length (table 2), with genes that
are on average twice as long as in other parts of the genome. Since longer proteins entail a greater metabolic cost
(Akashi 1996), protein length can increase when selective
constraints are relaxed, so that small insertions into coding
regions that have very small effects on fitness can then be
fixed more frequently. Weaker translational selection has
been proposed as the cause of the longer genes observed
in D. melanogaster than in D. simulans, as a result of the
apparently smaller effective population size along the D.
melanogaster lineage compared with that in D. simulans
(Akashi 1996). It is possible that this process has affected
the fourth chromosome but not the other nonrecombining
regions, consistent with the other evidence that it has been
subject to more intense HRI.
GBE
Molecular Evolution in Nonrecombining Regions
Conclusion
We have found only small differences among genes with
different frequencies of crossing-over in the regions of
the genome that have crossing-over. All regions that lacked
crossing-over showed at least some effects of the type expected from increased HRI: an accelerated rate of protein
sequence evolution, lower codon usage bias, and lower
GC content in both coding regions and introns. However,
there were some significant differences among nonrecombining regions. In general, the nonrecombining genes on the
X chromosome show less severe effects, whereas the fourth
chromosome and the autosomal genes located most proximal to the centromeres exhibited the most intense effects
of HRI. Nonetheless, there was evidence for some residual
effects of selection acting on nonrecombining genes.
Supplementary Material
Supplementary tables 1–4 and figure 1 are available at
Genome Biology and Evolution online (http://www.gbe.
oxfordjournals.org/).
Acknowledgments
We gratefully acknowledge Pablo Librado and Filipe Vieira
for help with the bioinformatic and statistical analyses and
for kindly providing computer code and Dan Halligan for
help with the statistical analyses. We thank members of
the Charlesworth lab group for helpful discussions and comments. We thank C. Smith and G. Karpen for advice on how
to annotate the heterochromatic genes. We are also grateful to two anonymous reviewers for their comments on the
manuscript. This work was supported by the UK Biotechnology and Biological Sciences Research Council (grant number
BB/H006028/1 to B.C.). P.R.H. is supported by a Fellowship
from the Natural Environment Research Council (grant number NE/G013195/1).
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